Text Recognition:
 With the rising applicability of deep learning methods,  Jaderberg [1]} employed convolution neural network for text recognition, but such methods were constrained to dictionary words. Although this limitation was eliminated by Jaderberg [2]}, it still needed huge resources for character level localisation. Sequence discriminative training using connectionist temporal classification (CTC) [3]} coupled with recurrent neural networks, dealt with the need for character level localisation. This lead to an end-to-end trainable convolutional recurrent neural network for reading texts [4]}. This was further enhanced by incorporating the idea of attention [5]} for text recognition [6]}, [7]}. Usually a two-fold process is implemented [8]}, [7]}, [10]}, [11]}, where an irregular image is first passed through a rectification network, and then followed by a text recognition network. Ideas like 2D attention mechanism, focusing attention network (FAN) [12]} has been explored recently along-with the possibility of enhancing text reading accuracy using synthetically generated large datasets [13]}. Despite such extensive research, reading irregular curved texts had not been explored much in details until recently in [14]}, which describes how arbitrarily oriented texts can be read by extracting four directional features from the 2D input image. Baek [15]} has conducted a comparative study where different popular text recognition architectures were trained in a similar setting. Hence it can be observed that although recent works in text reading have emphasised on designing a better rectification network [11]}, [8]}, all such researches [18]}, [19]} essentially used an off-the-shelf [4]} recognition module. On the other side, handwriting recognition poses a tougher challenge owing to a free flow nature [21]} of writing. Poznanski [22]} employed a ConvNet to estimate an n-gram frequency profile along with a large dictionary having true frequency profiles for recognition.
